Fish Audio▌
by da-okazaki
Convert text to speech with Fish Audio. Use our AI voice generator for real-time, high-quality speech to voice, free for
Integrates with Fish Audio's API to generate high-quality speech from text with configurable voice models, audio formats, and real-time streaming for creating conversational applications and automated content narration.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Building conversational AI applications
- / Automated content narration and voiceovers
- / Creating multilingual speech synthesis
- / Real-time voice generation for chatbots
capabilities
- / Generate speech from text using AI voice models
- / Stream audio in real-time for low-latency applications
- / Select voices by ID, name, or tags from voice library
- / Export audio in multiple formats (MP3, WAV, PCM, Opus)
- / Clone and create custom voice models
- / Control speech prosody and emotions
what it does
Converts text to speech using Fish Audio's API with support for multiple voice models, streaming, and various audio formats.
about
Fish Audio is a community-built MCP server published by da-okazaki that provides AI assistants with tools and capabilities via the Model Context Protocol. Convert text to speech with Fish Audio. Use our AI voice generator for real-time, high-quality speech to voice, free for It is categorized under ai ml.
how to install
You can install Fish Audio in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Fish Audio is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Fish Audio MCP Server
<div align="center"> <img src="./dcos/icon_fish-audio.webp" alt="Fish Audio Logo" width="300" height="300" /> </div>An MCP (Model Context Protocol) server that provides seamless integration between Fish Audio's Text-to-Speech API and LLMs like Claude, enabling natural language-driven speech synthesis.
What is Fish Audio?
Fish Audio is a cutting-edge Text-to-Speech platform that offers:
- 🌊 State-of-the-art voice synthesis with natural-sounding output
- 🎯 Voice cloning capabilities to create custom voice models
- 🌍 Multilingual support including English, Japanese, Chinese, and more
- ⚡ Low-latency streaming for real-time applications
- 🎨 Fine-grained control over speech prosody and emotions
This MCP server brings Fish Audio's powerful capabilities directly to your LLM workflows.
Features
- 🎙️ High-Quality TTS: Leverage Fish Audio's state-of-the-art TTS models
- 🌊 Streaming Support: Real-time audio streaming for low-latency applications
- 🎨 Multiple Voices: Support for custom voice models via reference IDs
- 🎯 Smart Voice Selection: Select voices by ID, name, or tags
- 📚 Voice Library Management: Configure and manage multiple voice references
- 🔧 Flexible Configuration: Environment variable-based configuration
- 📦 Multiple Audio Formats: Support for MP3, WAV, PCM, and Opus
- 🚀 Easy Integration: Simple setup with any MCP-compatible client
Quick Start
Installation
You can run this MCP server directly using npx:
npx @alanse/fish-audio-mcp-server
Or install it globally:
npm install -g @alanse/fish-audio-mcp-server
Configuration
-
Get your Fish Audio API key from Fish Audio
-
Set up environment variables:
export FISH_API_KEY=your_fish_audio_api_key_here
- Add to your MCP settings configuration:
Single Voice Mode (Simple)
{
"mcpServers": {
"fish-audio": {
"command": "npx",
"args": ["-y", "@alanse/fish-audio-mcp-server"],
"env": {
"FISH_API_KEY": "your_fish_audio_api_key_here",
"FISH_MODEL_ID": "speech-1.6",
"FISH_REFERENCE_ID": "your_voice_reference_id_here",
"FISH_OUTPUT_FORMAT": "mp3",
"FISH_STREAMING": "false",
"FISH_LATENCY": "balanced",
"FISH_MP3_BITRATE": "128",
"FISH_AUTO_PLAY": "false",
"AUDIO_OUTPUT_DIR": "~/.fish-audio-mcp/audio_output"
}
}
}
}
Multiple Voice Mode (Advanced)
{
"mcpServers": {
"fish-audio": {
"command": "npx",
"args": ["-y", "@alanse/fish-audio-mcp-server"],
"env": {
"FISH_API_KEY": "your_fish_audio_api_key_here",
"FISH_MODEL_ID": "speech-1.6",
"FISH_REFERENCES": "[{'reference_id':'id1','name':'Alice','tags':['female','english']},{'reference_id':'id2','name':'Bob','tags':['male','japanese']},{'reference_id':'id3','name':'Carol','tags':['female','japanese','anime']}]",
"FISH_DEFAULT_REFERENCE": "id1",
"FISH_OUTPUT_FORMAT": "mp3",
"FISH_STREAMING": "false",
"FISH_LATENCY": "balanced",
"FISH_MP3_BITRATE": "128",
"FISH_AUTO_PLAY": "false",
"AUDIO_OUTPUT_DIR": "~/.fish-audio-mcp/audio_output"
}
}
}
}
Environment Variables
| Variable | Description | Default | Required |
|---|---|---|---|
FISH_API_KEY | Your Fish Audio API key | - | Yes |
FISH_MODEL_ID | TTS model to use (s1, speech-1.5, speech-1.6) | s1 | Optional |
FISH_REFERENCE_ID | Default voice reference ID (single reference mode) | - | Optional |
FISH_REFERENCES | Multiple voice references (see below) | - | Optional |
FISH_DEFAULT_REFERENCE | Default reference ID when using multiple references | - | Optional |
FISH_OUTPUT_FORMAT | Default audio format (mp3, wav, pcm, opus) | mp3 | Optional |
FISH_STREAMING | Enable streaming mode (HTTP/WebSocket) | false | Optional |
FISH_LATENCY | Latency mode (normal, balanced) | balanced | Optional |
FISH_MP3_BITRATE | MP3 bitrate (64, 128, 192) | 128 | Optional |
FISH_AUTO_PLAY | Auto-play audio and enable real-time playback | false | Optional |
AUDIO_OUTPUT_DIR | Directory for audio file output | ~/.fish-audio-mcp/audio_output | Optional |
Configuring Multiple Voice References
You can configure multiple voice references in two ways:
JSON Array Format (Recommended)
Use the FISH_REFERENCES environment variable with a JSON array:
FISH_REFERENCES='[
{"reference_id":"id1","name":"Alice","tags":["female","english"]},
{"reference_id":"id2","name":"Bob","tags":["male","japanese"]},
{"reference_id":"id3","name":"Carol","tags":["female","japanese","anime"]}
]'
FISH_DEFAULT_REFERENCE="id1"
Individual Format (Backward Compatibility)
Use numbered environment variables:
FISH_REFERENCE_1_ID=id1
FISH_REFERENCE_1_NAME=Alice
FISH_REFERENCE_1_TAGS=female,english
FISH_REFERENCE_2_ID=id2
FISH_REFERENCE_2_NAME=Bob
FISH_REFERENCE_2_TAGS=male,japanese
Usage
Once configured, the Fish Audio MCP server provides two tools to LLMs.
Tool 1: fish_audio_tts
Generates speech from text using Fish Audio's TTS API.
Parameters
text(required): Text to convert to speech (max 10,000 characters)reference_id(optional): Voice model reference IDreference_name(optional): Select voice by namereference_tag(optional): Select voice by tagstreaming(optional): Enable streaming modeformat(optional): Output format (mp3, wav, pcm, opus)mp3_bitrate(optional): MP3 bitrate (64, 128, 192)normalize(optional): Enable text normalization (default: true)latency(optional): Latency mode (normal, balanced)output_path(optional): Custom output file pathauto_play(optional): Automatically play the generated audiowebsocket_streaming(optional): Use WebSocket streaming instead of HTTPrealtime_play(optional): Play audio in real-time during WebSocket streaming
Voice Selection Priority: reference_id > reference_name > reference_tag > default
Tool 2: fish_audio_list_references
Lists all configured voice references.
Parameters
No parameters required.
Returns
- List of configured voice references with their IDs, names, and tags
- Default reference ID
Examples
Basic Text-to-Speech
User: "Generate speech saying 'Hello, world! Welcome to Fish Audio TTS.'"
Claude: I'll generate speech for that text using Fish Audio TTS.
[Uses fish_audio_tts tool with text parameter]
Result: Audio file saved to ./audio_output/tts_2025-01-03T10-30-00.mp3
Using Custom Voice by ID
User: "Generate speech with voice model xyz123 saying 'This is a custom voice test'"
Claude: I'll generate speech using the specified voice model.
[Uses fish_audio_tts tool with text and reference_id parameters]
Result: Audio generated with custom voice model xyz123
Using Voice by Name
User: "Use Alice's voice to say 'Hello from Alice'"
Claude: I'll generate speech using Alice's voice.
[Uses fish_audio_tts tool with reference_name: "Alice"]
Result: Audio generated with Alice's voice
Using Voice by Tag
User: "Generate Japanese speech saying 'こんにちは' with an anime voice"
Claude: I'll generate Japanese speech with an anime-style voice.
[Uses fish_audio_tts tool with reference_tag: "anime"]
Result: Audio generated with anime voice style
List Available Voices
User: "What voices are available?"
Claude: I'll list all configured voice references.
[Uses fish_audio_list_references tool]
Result:
- Alice (id: id1) - Tags: female, english [Default]
- Bob (id: id2) - Tags: male, japanese
- Carol (id: id3) - Tags: female, japanese, anime
HTTP Streaming Mode
User: "Generate a long speech in streaming mode about the benefits of AI"
Claude: I'll generate the speech in streaming mode for faster response.
[Uses fish_audio_tts tool with streaming: true]
Result: Streaming audio saved to ./audio_output/tts_2025-01-03T10-35-00.mp3
WebSocket Real-time Streaming
User: "Stream and play in real-time: 'Welcome to the future of AI'"
Claude: I'll stream the speech via WebSocket and play it in real-time.
[Uses fish_audio_tts tool with websocket_streaming: true, realtime_play: true]
Result: Audio streamed and played in real-time via WebSocket
Development
Local Development
- Clone the repository:
git clone https://github.com/da-okazaki/mcp-fish-audio-server.git
cd mcp-fish-audio-server
- Install dependencies:
npm install
- Create
.envfile:
cp .env.example .env
# Edit .env with your API key
- Build the project:
npm run build
- Run in development mode:
npm run dev
Testing
Run the test suite:
npm test
Project Structure
mcp-fish-audio-server/
├── src/
│ ├── index.ts # MCP server entry point
│ ├── tools/
│ │ └── tts.ts # TTS tool implementation
│ ├── services/
│ │ └── fishAudio.ts # Fish Audio API client
│ ├── types/
│ │ └── index.ts # TypeScript definitions
│ └── utils/
│ └── config.ts # Configuration management
├── tests/ # Test files
├── audio_output/ # Default audio output directory
├── package.json
├── tsconfig.json
└── README.md
API Documentation
Fish Audio Service
The service provides two main methods:
-
generateSpeech: Standard TTS generation
- Returns audio buffer
- Suitable for short texts
- Lower memory usage
-
generateSpeechStream: Streamin
FAQ
- What is the Fish Audio MCP server?
- Fish Audio is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Fish Audio?
- This profile displays 46 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★46 reviews- ★★★★★Neel Menon· Dec 28, 2024
Fish Audio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Pratham Ware· Dec 24, 2024
I recommend Fish Audio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Zara Huang· Dec 12, 2024
Fish Audio reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Sakura Gill· Dec 4, 2024
Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Hiroshi Verma· Nov 23, 2024
I recommend Fish Audio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Kabir Liu· Nov 19, 2024
According to our notes, Fish Audio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Sakshi Patil· Nov 15, 2024
Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Neel Kim· Nov 15, 2024
We evaluated Fish Audio against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Hiroshi Tandon· Nov 3, 2024
Fish Audio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Hiroshi Patel· Oct 22, 2024
Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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